SoroushVahidi/scidocs huggingface dataset:
Generalized vulnerability extrapolation using abstract syntax trees
(2012)

The discovery of vulnerabilities in source code is a key for securing computer systems. While specific types of security flaws can be identified automatically, in the general case the process of finding vulnerabilities cannot be automated and vulnerabilities are mainly discovered by manual analysis. In this paper, we propose a method for assisting a security analyst during auditing of source code. Our method proceeds by extracting abstract syntax trees from the code and determining structural patterns in these trees, such that each function in the code can be described as a mixture of these patterns. This representation enables us to decompose a known vulnerability and extrapolate it to a code base, such that functions potentially suffering from the same flaw can be suggested to the analyst. We evaluate our method on the source code of four popular open-source projects: LibTIFF, FFmpeg, Pidgin and Asterisk. For three of these projects, we are able to identify zero-day vulnerabilities by inspecting only a small fraction of the code bases.
A Combinatorial Noise Model for Quantum Computer Simulation
(2007)

Quantum computers (QCs) have many potential hardware implementations ranging from solid-state silicon-based structures to electron-spin qubits on liquid helium. However, all QCs must contend with gate infidelity and qubit state decoherence over time. Quantum error correcting codes (QECCs) have been developed to protect program qubit states from such noise. Previously, Monte Carlo noise simulators have been developed to model the effectiveness of QECCs in combating decoherence. The downside to this random sampling approach is that it may take days or weeks to produce enough samples for an accurate measurement. We present an alternative noise modeling approach that performs combinatorial analysis rather than random sampling. This model tracks the progression of the most likely error states of the quantum program through its course of execution. This approach has the potential for enormous speedups versus the previous Monte Carlo methodology. We have found speedups with the combinatorial model on the order of 100X-1,000X over the Monte Carlo approach when analyzing applications utilizing the [[7,1,3]] QECC. The combinatorial noise model has significant memory requirements, and we analyze its scaling properties relative to the size of the quantum program. Due to its speedup, this noise model is a valuable alternative to traditional Monte Carlo simulation.
Random Walk with Restart on Large Graphs Using Block Elimination
(2016)

Given a large graph, how can we calculate the relevance between nodes fast and accurately? Random walk with restart (RWR) provides a good measure for this purpose and has been applied to diverse data mining applications including ranking, community detection, link prediction, and anomaly detection. Since calculating RWR from scratch takes a long time, various preprocessing methods, most of which are related to inverting adjacency matrices, have been proposed to speed up the calculation. However, these methods do not scale to large graphs because they usually produce large dense matrices that do not fit into memory. In addition, the existing methods are inappropriate when graphs dynamically change because the expensive preprocessing task needs to be computed repeatedly.
 In this article, we propose Bear, a fast, scalable, and accurate method for computing RWR on large graphs. Bear has two versions: a preprocessing method BearS for static graphs and an incremental update method BearD for dynamic graphs. BearS consists of the preprocessing step and the query step. In the preprocessing step, BearS reorders the adjacency matrix of a given graph so that it contains a large and easy-to-invert submatrix, and precomputes several matrices including the Schur complement of the submatrix. In the query step, BearS quickly computes the RWR scores for a given query node using a block elimination approach with the matrices computed in the preprocessing step. For dynamic graphs, BearD efficiently updates the changed parts in the preprocessed matrices of BearS based on the observation that only small parts of the preprocessed matrices change when few edges are inserted or deleted. Through extensive experiments, we show that BearS significantly outperforms other state-of-the-art methods in terms of preprocessing and query speed, space efficiency, and accuracy. We also show that BearD quickly updates the preprocessed matrices and immediately computes queries when the graph changes.
An energy-efficient unequal clustering mechanism for wireless sensor networks
(2005)

Clustering provides an effective way for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques, selecting cluster heads with more residual energy and rotating cluster heads periodically, to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, they rarely consider the hot spots problem in multihop wireless sensor networks. When cluster heads cooperate with each other to forward their data to the base station, the cluster heads closer to the base station are burdened with heavy relay traffic and tend to die early, leaving areas of the network uncovered and causing network partition. To address the problem, we propose an energy-efficient unequal clustering (EEUC) mechanism for periodical data gathering in wireless sensor networks. It partitions the nodes into clusters of unequal size, and clusters closer to the base station have smaller sizes than those farther away from the base station. Thus cluster heads closer to the base station can preserve some energy for the inter-cluster data forwarding. We also propose an energy-aware multihop routing protocol for the inter-cluster communication. Simulation results show that our unequal clustering mechanism balances the energy consumption well among all sensor nodes and achieves an obvious improvement on the network lifetime
Learning Bound for Parameter Transfer Learning
(2016)

We consider a transfer-learning problem by using the parameter transfer approach, where a suitable parameter of feature mapping is learned through one task and applied to another objective task. Then, we introduce the notion of the local stability and parameter transfer learnability of parametric feature mapping, and thereby derive a learning bound for parameter transfer algorithms. As an application of parameter transfer learning, we discuss the performance of sparse coding in selftaught learning. Although self-taught learning algorithms with plentiful unlabeled data often show excellent empirical performance, their theoretical analysis has not been studied. In this paper, we also provide the first theoretical learning bound for self-taught learning.
Analyzing Posture and Affect in Task-Oriented Tutoring
(2012)

Intelligent tutoring systems research aims to produce systems that meet or exceed the effectiveness of one on one expert human tutoring. Theory and empirical study suggest that affective states of the learner must be addressed to achieve this goal. While many affective measures can be utilized, posture offers the advantages of non intrusiveness and ease of interpretation. This paper presents an accurate posture estimation algorithm applied to a computer mediated tutoring corpus of depth recordings. Analyses of posture and session level student reports of engagement and cognitive load identified significant patterns. The results indicate that disengagement and frustration may coincide with closer postural positions and more movement, while focused attention and less frustration occur with more distant, stable postural positions. It is hoped that this work will lead to intelligent tutoring systems that recognize a greater breadth of affective expression through channels of posture and gesture.
Contributions of the prefrontal cortex to the neural basis of human decision making
(2002)

The neural basis of decision making has been an elusive concept largely due to the many subprocesses associated with it. Recent efforts involving neuroimaging, neuropsychological studies, and animal work indicate that the prefrontal cortex plays a central role in several of these subprocesses. The frontal lobes are involved in tasks ranging from making binary choices to making multi-attribute decisions that require explicit deliberation and integration of diverse sources of information. In categorizing different aspects of decision making, a division of the prefrontal cortex into three primary regions is proposed. (1) The orbitofrontal and ventromedial areas are most relevant to deciding based on reward values and contribute affective information regarding decision attributes and options. (2) Dorsolateral prefrontal cortex is critical in making decisions that call for the consideration of multiple sources of information, and may recruit separable areas when making well defined versus poorly defined decisions. (3) The anterior and ventral cingulate cortex appear especially relevant in sorting among conflicting options, as well as signaling outcome-relevant information. This topic is broadly relevant to cognitive neuroscience as a discipline, as it generally comprises several aspects of cognition and may involve numerous brain regions depending on the situation. The review concludes with a summary of how these regions may interact in deciding and possible future research directions for the field.
Image-based Airborne LiDAR Point Cloud Encoding for 3 D Building Model Retrieval
(2016)

With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority over related methods.
THE ROLE OF DIGITAL AND SOCIAL MEDIA MARKETING IN CONSUMER BEHAVIOR
(2015)

This article reviews recently published research about consumers in digital and social media marketing settings. Five themes are identified: (i) consumer digital culture, (ii) responses to digital advertising, (iii) effects of digital environments on consumer behavior, (iv) mobile environments, and (v) online word of mouth (WOM). Collectively these articles shed light from many different angles on how consumers experience, influence, and are influenced by the digital environments in which they are situated as part of their daily lives. Much is still to be understood, and existing knowledge tends to be disproportionately focused on WOM, which is only part of the digital consumer experience. Several directions for future research are advanced to encourage researchers to consider a broader range of phenomena.
A Spreadsheet Algebra for a Direct Data Manipulation Query Interface
(2009)

A spreadsheet-like "direct manipulation" interface is more intuitive for many non-technical database users compared to traditional alternatives, such as visual query builders. The construction of such a direct manipulation interfacemay appear straightforward, but there are some significant challenges. First, individual direct manipulation operations cannot be too complex, so expressive power has to be achieved through composing (long) sequences of small operations. Second, all intermediate results are visible to the user, so grouping and ordering are material after every small step. Third, users often find the need to modify previously specified queries. Since manipulations are specified one step at a time, there is no actual queryexpression to modify. Suitable means must be provided to address this need. Fourth, the order in which manipulations are performed by the user should not affect the results obtained, to avoid user confusion. We address the aforementioned challenges by designing a new spreadsheet algebra that: i) operates on recursively grouped multi-sets, ii) contains a selectively designed set of operators capable of expressing at least all single-block SQL queries and can be intuitively implemented in a spreadsheet, iii) enables query modification by the notion of modifiable query state, and iv) requires no ordering in unary data manipulation operators since they are all designed to commute. We built a prototype implementation of the spreadsheet algebra and show, through user studies with non-technical subjects, that the resultant query interface is easier to use than a standard commercial visual query builder.
An empirical study on dependence clusters for effort-aware fault-proneness prediction
(2016)

A dependence cluster is a set of mutually inter-dependent program elements. Prior studies have found that large dependence clusters are prevalent in software systems. It has been suggested that dependence clusters have potentially harmful effects on software quality. However, little empirical evidence has been provided to support this claim. The study presented in this paper investigates the relationship between dependence clusters and software quality at the function-level with a focus on effort-aware fault-proneness prediction. The investigation first analyzes whether or not larger dependence clusters tend to be more fault-prone. Second, it investigates whether the proportion of faulty functions inside dependence clusters is significantly different from the proportion of faulty functions outside dependence clusters. Third, it examines whether or not functions inside dependence clusters playing a more important role than others are more fault-prone. Finally, based on two groups of functions (i.e., functions inside and outside dependence clusters), the investigation considers a segmented fault-proneness prediction model. Our experimental results, based on five well-known open-source systems, show that (1) larger dependence clusters tend to be more fault-prone; (2) the proportion of faulty functions inside dependence clusters is significantly larger than the proportion of faulty functions outside dependence clusters; (3) functions inside dependence clusters that play more important roles are more fault-prone; (4) our segmented prediction model can significantly improve the effectiveness of effort-aware fault-proneness prediction in both ranking and classification scenarios. These findings help us better understand how dependence clusters influence software quality.
Reinforcement Learning in Continuous State and Action Spaces
(2012)

Many traditional reinforcement-learning algorithms have been designed for problems with small finite state and action spaces. Learn ing in such discrete problems can been difficult, due to noise and delayed reinfor cements. However, many real-world problems have continuous state or action sp aces, which can make learning a good decision policy even more involved. In this c apter we discuss how to automatically find good decision policies in continuous d omains. Because analytically computing a good policy from a continuous model c an be infeasible, in this chapter we mainly focus on methods that explicitly up date a representation of a value function, a policy or both. We discuss conside rations in choosing an appropriate representation for these functions and disc uss gradient-based and gradient-free ways to update the parameters. We show how to a pply these methods to reinforcement-learning problems and discuss many speci fic algorithms. Amongst others, we cover gradient-based temporal-difference lear ning, evolutionary strategies, policy-gradient algorithms and (natural) actor-cri ti methods. We discuss the advantages of different approaches and compare the perform ance of a state-of-theart actor-critic method and a state-of-the-art evolutiona ry strategy empirically.
Decision Support based on Bio-PEPA Modeling and Decision Tree Induction: A New Approach, Applied to a Tuberculosis Case Study
(2017)

The problem of selecting determinant features generating appropriate model structure is a challenge in epidemiological modelling. Disease spread is highly complex, and experts develop their understanding of its dynamic over years. There is an increasing variety and volume of epidemiological data which adds to the potential confusion. The authors propose here to make use of that data to better understand disease systems. Decision tree techniques have been extensively used to extract pertinent information and improve decision making. In this paper, the authors propose an innovative structured approach combining decision tree induction with Bio-PEPA computational modelling, and illustrate the approach through application to tuberculosis. By using decision tree induction, the enhanced Bio-PEPA model shows considerable improvement over the initial model with regard to the simulated results matching observed data. The key finding is that the developer expresses a realistic predictive model using relevant features, thus considering this approach as decision support, empowers the epidemiologist in his policy decision making. KEywoRDS Bio-PEPA Modelling, Data Mining, Decision Support, Decision Tree Induction, Epidemiology, Modelling and Simulation, Optimisation, Refinement, Tuberculosis
On Optimizing VLC Networks for Downlink Multi-User Transmission: A Survey
(2018)

The evolving explosion in high data rate services and applications will soon require the use of untapped, abundant unregulated spectrum of the visible light for communications to adequately meet the demands of the fifth-generation (5G) mobile technologies. Radio-frequency (RF) networks are proving to be scarce to cover the escalation in data rate services. Visible light communication (VLC) has emerged as a great potential solution, either in replacement of, or complement to, existing RF networks, to support the projected traffic demands. Despite of the prolific advantages of VLC networks, VLC faces many challenges that must be resolved in the near future to achieve a full standardization and to be integrated to future wireless systems. Here, we review the new, emerging research in the field of VLC networks and lay out the challenges, technological solutions, and future work predictions. Specifically, we first review the VLC channel capacity derivation, discuss the performance metrics and the associated variables; the optimization of VLC networks are also discussed, including resources and power allocation techniques, user-to-access point (AP) association and APs-toclustered-users-association, APs coordination techniques, nonorthogonal multiple access (NOMA) VLC networks, simultaneous energy harvesting and information transmission using the visible light, and the security issue in VLC networks. Finally, we propose several open research problems to optimize the various VLC networks by maximizing either the sum rate, fairness, energy efficiency, secrecy rate, or harvested energy.
Comparison of CoAP and MQTT Performance Over Capillary Radios
(2018)

The IoT protocols used in the application layer, namely the Constraint Application Protocol (CoAP) and Message Queue Telemetry Transport (MQTT) have dependencies to the transport layer. The choice of transport, Transmission Control Protocol (TCP) or the User Datagram Protocol(UDP), on the other hand, has an impact on the Internet of Things (IoT) application level performance, especially over a wireless medium. The motivation of this work is to look at the impact of the protocol stack on performance over two different wireless medium realizations, namely Bluetooth Low Energy and Wi-Fi. The use case studied is infrequent small reports sent from the sensor device to a central cloud storage over a last mile radio access link. We find that while CoAP/UDP based transport performs consistently better both in terms of latency and power consumption over both links, MQTT/TCP may also work when the use requirements allow for longerlatency providing better reliability. All in all, the full connectivity stack needs to be considered when designing an IoT deployment.
Design of a compact high power phased array for 5G FD-MIMO system at 29 GHz
(2016)

This paper presents a new design concept of a beam steerable high gain phased array antenna based on WR28 waveguide at 29 GHz frequency for fifth generation (5G) full dimension multiple input multiple output (FD-MIMO) system. The 8×8 planar phased array is fed by a three dimensional beamformer to obtain volumetric beam scanning ranging from −60 to +60 degrees both in azimuth and elevation direction. Beamforming network (BFN) is designed using 16 set of 8×8 Butler matrix beamformer to get 64 beam states, which control the horizontal and vertical angle. This is a new concept to design waveguide based high power three-dimensional beamformer for volumetric multibeam in Ka band for 5G application. The maximum gain of phased array is 28.5 dBi that covers 28.9 GHz to 29.4 GHz frequency band.
Development and evaluation of low cost game-based balance rehabilitation tool using the microsoft kinect sensor
(2011)

The use of the commercial video games as rehabilitation tools, such as the Nintendo WiiFit, has recently gained much interest in the physical therapy arena. Motion tracking controllers such as the Nintendo Wiimote are not sensitive enough to accurately measure performance in all components of balance. Additionally, users can figure out how to "cheat" inaccurate trackers by performing minimal movement (e.g. wrist twisting a Wiimote instead of a full arm swing). Physical rehabilitation requires accurate and appropriate tracking and feedback of performance. To this end, we are developing applications that leverage recent advances in commercial video game technology to provide full-body control of animated virtual characters. A key component of our approach is the use of newly available low cost depth sensing camera technology that provides markerless full-body tracking on a conventional PC. The aim of this research was to develop and assess an interactive game-based rehabilitation tool for balance training of adults with neurological injury.